r-trees: an average case analysis. r-trees - performance analysis how many disk (=node) accesses...

R-trees: An Average Case Analysis

R-trees - performance analysis

How many disk (=node) accesses we’ll need for range nn spatial joins

why does it matter?

R-trees - performance analysis

A: because we can design split etc algorithms accordingly; also, do query-optimization

motivating question: on, e.g., split, should we try to minimize the area (volume)? the perimeter? the overlap? or a weighted combination? why?

R-trees - performance analysis

How many disk accesses (expected value) for range queries? query distribution wrt location? “ “ wrt size?

R-trees - performance analysis

How many disk accesses for range queries? query distribution wrt location? uniform; (biased) “ “ wrt size? uniform

R-trees - performance analysis

easier case: we know the positions of data nodes and their MBRs, eg:

R-trees - performance analysis

How many times will P1 be retrieved (unif. queries)?

P1

x1

x2

R-trees - performance analysis

How many times will P1 be retrieved (unif. POINT queries)?

P1

x1

x2

0 10

1

R-trees - performance analysis

How many times will P1 be retrieved (unif. POINT queries)? A: x1*x2

P1

x1

x2

0 10

1

R-trees - performance analysis

How many times will P1 be retrieved (unif. queries of size q1xq2)?

P1

x1

x2

0 10

1

q1

q2

R-trees - performance analysis Minkowski sum

q1

q2

q1/2

q2/2

R-trees - performance analysis

How many times will P1 be retrieved (unif. queries of size q1xq2)? A: (x1+q1)*(x2+q2)

P1

x1

x2

0 10

1

q1

q2

R-trees - performance analysis

Thus, given a tree with n nodes (i=1, ... n) we expect

))((),( 22,11,21 qxqxqqDA i

n

ii

2,1, i

n

ii xx

1,22,1 i

n

ii

n

i

xqxq

nqq 21

R-trees - performance analysis

Thus, given a tree with n nodes (i=1, ... n) we expect

‘volume’

‘surface area’

count

))((),( 22,11,21 qxqxqqDA i

n

ii

2,1, i

n

ii xx

1,22,1 i

n

ii

n

i

xqxq

nqq 21

R-trees - performance analysis

Observations: for point queries: only volume matters for horizontal-line queries: (q2=0): vertical length

matters for large queries (q1, q2 >> 0): the count N

matters overlap: does not seem to matter (but it is related

to area) formula: easily extendible to n dimensions

R-trees - performance analysis

Conclusions: splits should try to minimize area and perimeter ie., we want few, small, square-like parent MBRs rule of thumb: shoot for queries with q1=q2 = 0.1

(or =0.05 or so).

More general Model What if we have only the dataset D and the

set of queries S? We should “predict” the structures of a “good”

R-tree for this dataset. Then use the previous model to estimate the average query performance for S

For point dataset, we can use the Fractal Dimension to find the “average” structure of the tree

(More in the [FK94] paper)

Unifrom dataset Assume that the dataset (that contains only

rectangles) is uniformly distributed in space. Density of a set of N MBRs is the average number

of MBRs that contain a given point in space. OR the total area covered by the MBRs over the area of the work space.

N boxes with average size s= (s1,s2), D(N,s) = N s1 s2

If s1=s2=s, then: N

DssND 2

Density of Leaf nodes Assume a dataset of N rectangles. If the average page capacity is f, then we have N ln = N/f

leaf nodes. If D1 is the density of the leaf MBRs, and the average area of each leaf MBR is s2, then:

So, we can estimate s1, from N, f, D1

We need to estimate D1 from the dataset’s density…

N

fDss

f

ND 11

211

Estimating D1

f

f

Consider a leaf node thatcontains f MBRs.Then for each side of the leaf node MBR we have: MBRs

Also, Nln leaf nodes contain N MBRs, uniformly distributed.The average distance between the centers of two consecutive MBRs is t= (assuming [0,1]2 space)

N

1

t

Estimating D1

Combining the previous observations we can estimate the density at the leaf level, from the density of the dataset:

We can apply the same ideas recursively to the other levels of the tree.

21 }

11{

f

DD

R-trees–performance analysis

Assuming Uniform distribution:

where And D is the density of the dataset, f the

fanout [TS96], N the number of objects

}){(1)( 21

1j

h

jj f

NqDqDA

DDandf

DD

jj

021

}1

1{

References Christos Faloutsos and Ibrahim Kamel. “Beyond Uniformity

and Independence: Analysis of R-trees Using the Concept of Fractal Dimension”. Proc. ACM PODS, 1994.

Yannis Theodoridis and Timos Sellis. “A Model for the Prediction of R-tree Performance”. Proc. ACM PODS, 1996.